An Analysis of Feature Selection Techniques For COVID-19 Detection on Chest X-Ray Data

Abstract

We are currently experiencing a worldwide health problem known as the coronavirus pandemic, many researchers are looking to help in any way they can to deal with the pandemic and the problems caused by it. In the context of machine learning research, it is possible to develop methods to assist with the screening of patients using different types of exams and machine learning techniques. In this paper, we investigate the use of different features selection methods with different classifiers to the task of covid-19 (and other five pathologies and healthy lungs) identification in chest x-rays images. The analysis of the experimental results shows that the application of feature selection methods can improve the detection of coronavirus as well as other pathologies.